Weaving Rules into [email protected] for Embedded Smart Systems

Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with actions, triggered by domain-related conditions. The challenge at hand is that smart systems usually need to process thousands of updates to detect which rules need to be triggered, often even on restricted hardware like a Raspberry Pi. Despite various approaches have been investigated to efficiently check conditions on data models, they either assume to fit into main memory or rely on high latency persistence storage systems that severely damage the reactivity of smart systems. To tackle this challenge, we propose a novel composition process, which weaves executable rules into a data model with lazy loading abilities. We quantitatively show, on a smart building case study, that our approach can handle, at low latency, big sets of rules on top of large-scale data models on restricted hardware.Comment: pre-print version, published in the proceedings of MOMO-17 Worksho

Nickel Isotope Variations in Terrestrial Silicate Rocks and Geological Reference Materials Measured by MC-ICP-MS

International audienceAlthough initial studies have demonstrated the applicability of Ni isotopes for cosmochemistry and as a potential biosignature, the Ni isotope composition of terrestrial igneous and sedimentary rocks, and ore deposits remains poorly known. Our contribution is fourfold: (a) to detail an analytical procedure for Ni isotope determination, (b) to determine the Ni isotope composition of various geological reference materials, (c) to assess the isotope composition of the Bulk Silicate Earth relative to the Ni isotope reference material NIST SRM 986 and (d) to report the range of mass-dependent Ni isotope fractionations in magmatic rocks and ore deposits. After purification through a two-stage chromatography procedure, Ni isotope ratios were measured by MC-ICP-MS and were corrected for instrumental mass bias using a double-spike correction method. Measurement precision (two standard error of the mean) was between 0.02 and 0.04‰, and intermediate measurement precision for NIST SRM 986 was 0.05‰ (2s). Igneous- and mantle-derived rocks displayed a restricted range of δ60/58Ni values between −0.13 and +0.16‰, suggesting an average BSE composition of +0.05‰. Manganese nodules (Nod A1; P1), shale (SDO-1), coal (CLB-1) and a metal-contaminated soil (NIST SRM 2711) showed positive values ranging between +0.14 and +1.06‰, whereas komatiite-hosted Ni-rich sulfides varied from −0.10 to −1.03‰

Surveillance épidémiologique des hernies discales opérées en lien avec l’activité professionnelle : Etudes en région Pays de la Loire

Introduction.– La hernie discale opérée (HDO) a été choisie comme indicateur de pathologie rachidienne pour déterminer l’association entre HDO et activité professionnelle et la contribution de celle-ci dans la survenue de cette pathologie. Méthodes.– Les patients furent sélectionnés à partir des bases régionales du Programme de médicalisation des systèmes d’information (PMSI) sur : âge 20 à 59 ans, acte d’HDO, en 2002 à 2003 au CHU de Nantes, pour l’étude pilote, et en 2007 à 2008 dans tous les centres chirurgicaux des Pays de la Loire, pour l’étude régionale. L’histoire médicale et professionnelle fut recueillie par auto-questionnaire postal. Le risque relatif ajusté sur l’âge (RR) permit d’estimer l’association entre HDO et activité professionnelle. La contribution des secteurs d’activité et professions au risque d’HDO fut quantifiée par la fraction de risque attribuable aux facteurs professionnels dans la population (Frap) et par la fraction de risque attribuable chez les exposés (Frae). Résultats.– L’étude pilote incluait 146 répondeurs à l’auto-questionnaire parmi les 272 patients éligibles. L’étude régionale comprenait 1870 répondants parmi les 3135 patients opérés en 2007 à 2008. Le RR d’HDO était, chez les hommes, de 5,1[2,6–10,1] dans les transports et, chez les femmes, de 2,7[1,6–4,5] dans la santé et de 10,2[4,4–23,3] dans l’hôtellerie restauration. Les fractions de risque attribuable les plus élevées étaient associées à l’hôtellerie restauration chez les femmes (Frap = 19 %, Frae = 90 %). Les professions à risque étaient, chez les hommes, les chauffeurs (Frap = 12 %, Frae = 79 %) et les ouvriers non qualifiés de l’industrie (Frap = 17 %, Frae = 80 %) et, chez les femmes, les employées de la fonction publique (Frap = 18 %, Frae = 66 %). Discussion et conclusion.– Ces études ont confirmé l’intérêt d’une surveillance de l’HDO fondée sur le PMSI et ont permis d’identifier les professions à risque d’HDO en population générale

Lucky Strike: is it a TAG (Trans-Atlantic Geotraverse) precursory hydrothermal system?

Trabalho apresentado em InterRidge Workshop on Hydrothermal Ore-forming Processes,19-22 Setembro de 2019, Hangzhou, ChinaN/

The Next Evolution of MDE: A Seamless Integration of Machine Learning into Domain Modeling

Machine learning algorithms are designed to resolve unknown behaviors by extracting commonalities over massive datasets. Unfortunately, learning such global behaviors can be inaccurate and slow for systems composed of heterogeneous elements, which behave very differently, for instance as it is the case for cyber-physical systems andInternet of Things applications. Instead, to make smart deci-sions, such systems have to continuously refine the behavior on a per-element basis and compose these small learning units together. However, combining and composing learned behaviors from different elements is challenging and requires domain knowledge. Therefore, there is a need to structure and combine the learned behaviors and domain knowledge together in a flexible way. In this paper we propose to weave machine learning into domain modeling. More specifically, we suggest to decompose machine learning into reusable, chainable, and independently computable small learning units, which we refer to as microlearning units.These micro learning units are modeled together with and at the same level as the domain data. We show, based on asmart grid case study, that our approach can be significantly more accurate than learning a global behavior, while the performance is fast enough to be used for live learning

Analyzing Complex Data in Motion at Scale with Temporal Graphs

Modern analytics solutions succeed to understand and predict phenomenons in a large diversity of software systems, from social networks to Internet-of-Things platforms. This success challenges analytics algorithms to deal with more and more complex data, which can be structured as graphs and evolve over time. However, the underlying data storage systems that support large-scale data analytics, such as time-series or graph databases, fail to accommodate both dimensions, which limits the integration of more advanced analysis taking into account the history of complex graphs, for example. This paper therefore introduces a formal and practical definition of temporal graphs. Temporal graphs pro- vide a compact representation of time-evolving graphs that can be used to analyze complex data in motion. In particular, we demonstrate with our open-source implementation, named GREYCAT, that the performance of temporal graphs allows analytics solutions to deal with rapidly evolving large-scale graphs

Syndrome du canal carpien : impact de la co-exposition à des agents neurotoxiques et à des contraintes biomécaniques

Le syndrome du canal carpien est l’une des principales causes de maladie professionnelle. Dans un grand nombre de situations de travail, les travailleurs sont confrontés à des expositions multiples, mais peu de données sont disponibles sur les co-expositions des travailleurs à des agents chimiques neurotoxiques et à des contraintes biomécaniques